{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from datetime import datetime\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "mpl.rcParams['axes.unicode_minus'] = False\n",
    "pd.set_option('display.float_format',lambda x : '%.2f' % x)#pandas禁用科学计数法\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = \"D:/edgeDownload/电子产品销售分析.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_time</th>\n",
       "      <th>order_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>category_id</th>\n",
       "      <th>category_code</th>\n",
       "      <th>brand</th>\n",
       "      <th>price</th>\n",
       "      <th>user_id</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>local</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-04-24 11:50:39 UTC</td>\n",
       "      <td>2294359932054536986</td>\n",
       "      <td>1515966223509089906</td>\n",
       "      <td>2268105426648171520</td>\n",
       "      <td>electronics.tablet</td>\n",
       "      <td>samsung</td>\n",
       "      <td>162.01</td>\n",
       "      <td>1515915625441993984</td>\n",
       "      <td>24.00</td>\n",
       "      <td>女</td>\n",
       "      <td>海南</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-04-24 11:50:39 UTC</td>\n",
       "      <td>2294359932054536986</td>\n",
       "      <td>1515966223509089906</td>\n",
       "      <td>2268105426648171520</td>\n",
       "      <td>electronics.tablet</td>\n",
       "      <td>samsung</td>\n",
       "      <td>162.01</td>\n",
       "      <td>1515915625441993984</td>\n",
       "      <td>24.00</td>\n",
       "      <td>女</td>\n",
       "      <td>海南</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-04-24 14:37:43 UTC</td>\n",
       "      <td>2294444024058086220</td>\n",
       "      <td>2273948319057183658</td>\n",
       "      <td>2268105430162997248</td>\n",
       "      <td>electronics.audio.headphone</td>\n",
       "      <td>huawei</td>\n",
       "      <td>77.52</td>\n",
       "      <td>1515915625447879424</td>\n",
       "      <td>38.00</td>\n",
       "      <td>女</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-04-24 14:37:43 UTC</td>\n",
       "      <td>2294444024058086220</td>\n",
       "      <td>2273948319057183658</td>\n",
       "      <td>2268105430162997248</td>\n",
       "      <td>electronics.audio.headphone</td>\n",
       "      <td>huawei</td>\n",
       "      <td>77.52</td>\n",
       "      <td>1515915625447879424</td>\n",
       "      <td>38.00</td>\n",
       "      <td>女</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-04-24 19:16:21 UTC</td>\n",
       "      <td>2294584263154074236</td>\n",
       "      <td>2273948316817424439</td>\n",
       "      <td>2268105471367840000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>karcher</td>\n",
       "      <td>217.57</td>\n",
       "      <td>1515915625443148032</td>\n",
       "      <td>32.00</td>\n",
       "      <td>女</td>\n",
       "      <td>广东</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                event_time             order_id           product_id  \\\n",
       "0  2020-04-24 11:50:39 UTC  2294359932054536986  1515966223509089906   \n",
       "1  2020-04-24 11:50:39 UTC  2294359932054536986  1515966223509089906   \n",
       "2  2020-04-24 14:37:43 UTC  2294444024058086220  2273948319057183658   \n",
       "3  2020-04-24 14:37:43 UTC  2294444024058086220  2273948319057183658   \n",
       "4  2020-04-24 19:16:21 UTC  2294584263154074236  2273948316817424439   \n",
       "\n",
       "           category_id                category_code    brand  price  \\\n",
       "0  2268105426648171520           electronics.tablet  samsung 162.01   \n",
       "1  2268105426648171520           electronics.tablet  samsung 162.01   \n",
       "2  2268105430162997248  electronics.audio.headphone   huawei  77.52   \n",
       "3  2268105430162997248  electronics.audio.headphone   huawei  77.52   \n",
       "4  2268105471367840000                          NaN  karcher 217.57   \n",
       "\n",
       "               user_id   age sex local  \n",
       "0  1515915625441993984 24.00   女    海南  \n",
       "1  1515915625441993984 24.00   女    海南  \n",
       "2  1515915625447879424 38.00   女    北京  \n",
       "3  1515915625447879424 38.00   女    北京  \n",
       "4  1515915625443148032 32.00   女    广东  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(file_path,index_col=0,dtype={'category_id':'int64','user_id':'int64'},encoding='utf8')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 探索分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>category_id</th>\n",
       "      <th>category_code</th>\n",
       "      <th>brand</th>\n",
       "      <th>price</th>\n",
       "      <th>user_id</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>local</th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2294359932054536986</td>\n",
       "      <td>1515966223509089906</td>\n",
       "      <td>2268105426648171520</td>\n",
       "      <td>electronics.tablet</td>\n",
       "      <td>samsung</td>\n",
       "      <td>162.01</td>\n",
       "      <td>1515915625441993984</td>\n",
       "      <td>24.00</td>\n",
       "      <td>女</td>\n",
       "      <td>海南</td>\n",
       "      <td>2020-04-24</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2294359932054536986</td>\n",
       "      <td>1515966223509089906</td>\n",
       "      <td>2268105426648171520</td>\n",
       "      <td>electronics.tablet</td>\n",
       "      <td>samsung</td>\n",
       "      <td>162.01</td>\n",
       "      <td>1515915625441993984</td>\n",
       "      <td>24.00</td>\n",
       "      <td>女</td>\n",
       "      <td>海南</td>\n",
       "      <td>2020-04-24</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2294444024058086220</td>\n",
       "      <td>2273948319057183658</td>\n",
       "      <td>2268105430162997248</td>\n",
       "      <td>electronics.audio.headphone</td>\n",
       "      <td>huawei</td>\n",
       "      <td>77.52</td>\n",
       "      <td>1515915625447879424</td>\n",
       "      <td>38.00</td>\n",
       "      <td>女</td>\n",
       "      <td>北京</td>\n",
       "      <td>2020-04-24</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2294444024058086220</td>\n",
       "      <td>2273948319057183658</td>\n",
       "      <td>2268105430162997248</td>\n",
       "      <td>electronics.audio.headphone</td>\n",
       "      <td>huawei</td>\n",
       "      <td>77.52</td>\n",
       "      <td>1515915625447879424</td>\n",
       "      <td>38.00</td>\n",
       "      <td>女</td>\n",
       "      <td>北京</td>\n",
       "      <td>2020-04-24</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2294584263154074236</td>\n",
       "      <td>2273948316817424439</td>\n",
       "      <td>2268105471367840000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>karcher</td>\n",
       "      <td>217.57</td>\n",
       "      <td>1515915625443148032</td>\n",
       "      <td>32.00</td>\n",
       "      <td>女</td>\n",
       "      <td>广东</td>\n",
       "      <td>2020-04-24</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              order_id           product_id          category_id  \\\n",
       "0  2294359932054536986  1515966223509089906  2268105426648171520   \n",
       "1  2294359932054536986  1515966223509089906  2268105426648171520   \n",
       "2  2294444024058086220  2273948319057183658  2268105430162997248   \n",
       "3  2294444024058086220  2273948319057183658  2268105430162997248   \n",
       "4  2294584263154074236  2273948316817424439  2268105471367840000   \n",
       "\n",
       "                 category_code    brand  price              user_id   age sex  \\\n",
       "0           electronics.tablet  samsung 162.01  1515915625441993984 24.00   女   \n",
       "1           electronics.tablet  samsung 162.01  1515915625441993984 24.00   女   \n",
       "2  electronics.audio.headphone   huawei  77.52  1515915625447879424 38.00   女   \n",
       "3  electronics.audio.headphone   huawei  77.52  1515915625447879424 38.00   女   \n",
       "4                          NaN  karcher 217.57  1515915625443148032 32.00   女   \n",
       "\n",
       "  local       date  month weekday  \n",
       "0    海南 2020-04-24      4       5  \n",
       "1    海南 2020-04-24      4       5  \n",
       "2    北京 2020-04-24      4       5  \n",
       "3    北京 2020-04-24      4       5  \n",
       "4    广东 2020-04-24      4       5  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['date'] = pd.to_datetime(data.event_time.apply(lambda x : x.split(' ')[0]))\n",
    "data['month'] = data.date.dt.month\n",
    "data['weekday'] = data.date.apply(lambda x:x.strftime(\"%w\"))\n",
    "del data['event_time']\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['category_code'] = data['category_code'].fillna(\"M\")\n",
    "#删除brand缺失行\n",
    "data = data[data.brand.notnull()]\n",
    "data.order_id = data.order_id.astype('object')\n",
    "data.product_id = data.product_id.astype('object')\n",
    "data.category_id = data.category_id.astype('object')\n",
    "data.user_id = data.user_id.astype('object')\n",
    "data['weekday'] = data.loc[:,'weekday'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>1%</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>99%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>536945.00</td>\n",
       "      <td>214.48</td>\n",
       "      <td>305.95</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.13</td>\n",
       "      <td>24.51</td>\n",
       "      <td>99.51</td>\n",
       "      <td>289.33</td>\n",
       "      <td>1387.01</td>\n",
       "      <td>11574.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>536945.00</td>\n",
       "      <td>33.18</td>\n",
       "      <td>10.12</td>\n",
       "      <td>16.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>33.00</td>\n",
       "      <td>42.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>536945.00</td>\n",
       "      <td>7.72</td>\n",
       "      <td>2.56</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday</th>\n",
       "      <td>536945.00</td>\n",
       "      <td>3.03</td>\n",
       "      <td>2.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count   mean    std   min    1%   25%   50%    75%     99%  \\\n",
       "price   536945.00 214.48 305.95  0.00  1.13 24.51 99.51 289.33 1387.01   \n",
       "age     536945.00  33.18  10.12 16.00 16.00 24.00 33.00  42.00   50.00   \n",
       "month   536945.00   7.72   2.56  1.00  1.00  6.00  8.00  10.00   11.00   \n",
       "weekday 536945.00   3.03   2.03  0.00  0.00  1.00  3.00   5.00    6.00   \n",
       "\n",
       "             max  \n",
       "price   11574.05  \n",
       "age        50.00  \n",
       "month      11.00  \n",
       "weekday     6.00  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(percentiles=[0.01,0.25,0.75,0.99]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[data.date>'1970-01-01'].rename(columns={\"price\":\"amount\"})\n",
    "data.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>local</th>\n",
       "      <th>用户数量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>湖南</td>\n",
       "      <td>5330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>天津</td>\n",
       "      <td>5337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>重庆</td>\n",
       "      <td>5342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>湖北</td>\n",
       "      <td>5355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>浙江</td>\n",
       "      <td>5370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>四川</td>\n",
       "      <td>5445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>海南</td>\n",
       "      <td>5449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>江苏</td>\n",
       "      <td>5561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>北京</td>\n",
       "      <td>15928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>上海</td>\n",
       "      <td>16031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>广东</td>\n",
       "      <td>21382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   local   用户数量\n",
       "0     湖南   5330\n",
       "1     天津   5337\n",
       "2     重庆   5342\n",
       "3     湖北   5355\n",
       "4     浙江   5370\n",
       "5     四川   5445\n",
       "6     海南   5449\n",
       "7     江苏   5561\n",
       "8     北京  15928\n",
       "9     上海  16031\n",
       "10    广东  21382"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "local = data.groupby('local')['user_id'].nunique().reset_index()\n",
    "local = local.rename(columns={'user_id':'用户数量'})\n",
    "local = local.sort_values('用户数量').reset_index(drop=True)\n",
    "local"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "plt.ylabel('用户数量')\n",
    "plt.title('各地区用户数量')\n",
    "plt.bar(local['local'], local['用户数量'])\n",
    "for x, y in enumerate(local['用户数量']):\n",
    "    plt.text(x, y+200, y, ha='center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOcAAADnCAYAAADl9EEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAUaklEQVR4nO3deZwcZZ3H8U91zxUmySThSkhCSrIBlCAJGjbIIkF2caFZDC4K4oJo8MBV12uxiK6UK0rjCiqicqwci6hhuaIUpyKLIglHYpAY5EiaOyGBoZLM3d3P/lE9YTLMne7+PVX9e79e85pJ0/16vkPy7aequuopxxiDUso+KekASqmBaTmVspSWUylLaTmVspSWUylLaTmVspSWUylLaTmVspSWUylLaTmVspSWUylLaTmVspSWUylLaTmVspSWM+Ecx0k5jnO04ziOdBY1OlrO5JsLfN3ohbuxo+VMvhOAq6RDqNFz9A01eRzHORS4GMgDhwB/BTqJ3oxTQBH4D2PMA2Ih1bC0nAnmOM5M4HJjzPGlP58DhMaYy2WTqZHQzdpk+xRwaZ8/Hw/cKpRFjVKddABVUa2A5zjOQcATwHpjzCbhTGqEdOZMMGPMd4H3EP093wI0Oo4zXTaVGimdORPKcZxG4FDgJGBe6WsmcLPjOA8AS40xnYIR1TD0gFBCOY7zHaIZc7kx5vd9Hk8Dpxhjfi4WTo2IllMpS+k+p1KW0nIqZSk9IGQx1wvGATOAvYGpfb73/XkS0d9jCkgDDtGZQd2lry5gC/Ai8ELpe9+vzblsRvdtLKT7nJZwvWAKML/P16HA/lR+66YbyAGPlL4eBlblspn2Co+rhqHlFOB6gQMsAI4F3kFUxlmioXZWANYRFfURYAWwWmfY6tJyVonrBROIyngCcBzRZmmcvAz8ClgO3JvLZrqE8ySelrOCXC/4G6IyngAcCTTIJiqbbcBdROfpBrls5nXhPImk5Swz1wuagA8AnwSOEI5TDXngPuBK4OZcNpOXjZMcWs4ycb1gf6JCfgTYXTiOlJeAK4ArctnMy9Jh4k7LuQtcL6gH3k9UyqOF49ikB7gZuDSXzfxBOkxcaTnHwPWCBuATwFJgmnAc260Bvg9cl8tmCtJh4kTLOQquF6SBM4DzsOujjzhYByzNZTN6sfcIaTlHoPS55MnAfwIHCseJuz8CX9HN3eFpOYfhesHxwPlEJwqo8vk1cG4um1krHcRWWs5BuF4wE7iMaN0dVRkF4FrAy2Uzm6XD2EbL2U9pE/YTwHeAicJxasVm4OxcNnOTdBCbaDn7cL1gNvDfwCLhKLVqGfCvuWzmVekgNtByAq4XpIDPA98EdhOOU+s2AZ/MZTPLpYNIq/lyul4wB/gfYKF0FrWTnwGfy2UzrdJBpNR0OV0vyADXAy3SWdSAXgI+mMtmavK2ETVZztJBn68C30CXarFdN9HBopq7GVPNldP1gvHANcA/C0dRo/M94N9r6RTAmipn6WjscuAg6SxqTO4ETs1lM6F0kGqomU061wveS7TshhYzvv4RWFE6iJd4NVFO1wvOBAJgsnAUtesOBFa6XnCMdJBKS3w5XS/4DNGdndPSWVTZTAZud73gROkglZTocrpecC7wQ6K1XFWyNAA3ul5wsnSQSklsOV0v+A/g29I5VEXVA790veBD0kEqIZFHa10vWAp8SzqHqpoC0VHcG6WDlFPiyul6wTnAhdI5VNX1ACflsplAOki5JKqcrhd8lOjgj6pNXcAJuWzmN9JByiEx5XS94N3APSRn4WY1NluBhblsZp10kF2ViHK6XrAfsBLYQzqLssJTwGFxX4k+9kdrXS+YSLQejRZzDAptrZhC4hZpnwMsK62WGFuxvj9n6X/+MuBt0lkqodDWyqYbzmPPk5bSes9lFLvbaZi2P1Pec9aAz9+2+nba1t0PQLGrjcZpBzBx4clveu3WR39N2+P3sveHLqBjwyrGz03kyTbHEh0Y/LJ0kLGK9Wat6wU/AD4nnaNSttx2EV0vP0XDni4TFyymcfqBbF5+IRPmH0fTvm8f8rWv3XMZzXOPYevKm9702ra199EwbQ71U6ZT6NhG8wGJvqXLGbls5jrpEGMR281a1wuWkOBidjy7Bqe+kXTzJHpaX6Rh6mwA0ru1UOwa+r62+W1bKLS9TuO0OQO+1hiDKeTp2LCacfu9o+K/i7ArXC9YIB1iLGJZztKt9S6RzlEpptBD+MdfMvmoMwHY7YAjeP2BX9D+9Eo6NjxK06xDhnz9tlUBE+YfP+hrx71lPh3PPEzdhD3YfNM36Xz2sUr/SpKagFtcL4jdMYnYlbO0GNe1JHghrnDFjUyYnyHVNB6ASe86lXH7vYPta+6mee4xpBrGDfpaY4p0PvcYjfsePOhrm9/6biYdcRqppmbGzV5A+5OJXwVkOtE51rESu3IS7eC/SzpEJXXm/sS2Vbex8ece3a9s4NU7LqFhr/3Ib93MxAWLh3xt1/NraZx2AI7zxrn+A722p/Ul6iZNw0nXE+fjDqNwqusFJ0mHGI1YldP1grlE9ytJtKkfvpCpp2WZelqWhr3ewu7HfY6tK29m4oLFpOqbAOje8hyt97/5OEfHhlU0zZy702P9X1vsaifdPJn63Weybc2djJs1r/K/lB1+4nrBFOkQIxWbo7Wle2GuAA6VzqJi7fpcNvMv0iFGIk4z59fQYqpd9+G4XKQdi5nT9YL5wEPE/KQJZY2XgYNsX7A6LjPnxWgxVflMAy6SDjEc62dO1wtOIDp3VqlyKgLzc9mMtR/yWj1zls6d1QunVSWksHwZG6vLCXyMhJ7UrqyQcb3g76RDDMbacrpe0Ex0LxOlKikrHWAw1pYT+BLRjrtSlXRE6biGdaw8IOR6wV7AM8B46SyqJvwZmJfLZorSQfqydeb8AlpMVT0HA6dJh+jPunK6XtAEfFw6h6o550oH6M+6chK9g+0uHULVnLe5XrBIOkRfNpbzs9IBVM06WzpAX1YdEHK94Ejgfukcqmb1APvmspmN0kHAvpkzsWsCqViox6LjHdbMnK4XzAA2oCe4K1kvAG4umylIB7Fp5jwbLaaSNwP4J+kQYFc5rfucSdUsKw4MWbFZ63rBPGC1dA6lSgrA1Fw2s0UyhC0zZ6xWRVOJlwaOlw6h5VRqYOLrDIlv1rpeMBt4WjSEUm+2Ddgjl810SwWwYebUWVPZaAJwtGQAG8r5fukASg1C9CMV0XK6XjAVWCiZQakh1G45gaMAZ9hnKSVjX9cLhr6lWwVJl/Mw4fGVGs5RUgNLl/NvhcdXajhidxcWK6frBXXovU+U/WqvnETrtgx+F1il7HCg6wUiN2qWLKfub6o4SAMiNzCVLKfub6q4ENm01ZlTqeHVTjlLB4MOlBhbqTGonXIC+xBtyysVB28t3fGuqqTKOVNoXKXGIg3sXe1BtZxKjUzVb6ql5VRqZPap9oBS5ZwhNK5SY6Uzp1KW0nIqZamaKed0oXGVGqua2eecIDSuUmNVMzNno9C4So1V1a9MqXo5XS9wiO7mpFScVP3frMSNgxp3o7OtQCpdIFWXJ50GR9cRUrareleqPmCu6bQ6oLnvY8ZgiO5PkS99LxgogFMwUDQ4pcecosEpfadYxDGGVKEYPW6KOIUiKVPEMcXocVMgZQomVSySMgVSFHjj5zzp6L+TJm9K33c8niZP2smTNnnS5E3a6SFN6XHypFM9vT+bdKqHut7n9z6eil5T5xRIp0qPO/nSz3kTfS+QSvVQlyqQSuVJp/OkUwWTSu/4OXoTSxdIp/UNTVTyywm8aYl5x8EpZdmRx9npqbu4Kn0C/xn3eUPb8aZWekOTvzNVAhVxtkJrVce0opxq9Pq9oTVC/zc0VU4pTFj9MZVSI5Gv9oAS5exG395V/NRAOf0wD7xe9XGV2jWd1R5QarP2FaFxlRqrjdUeUMup1Mi8VO0BtZxKjczL1R5Qy6nUyGg5lbKUbtYqZamamTmrfuRLqV1UM+X8q9C4So2FocbKWfUPdZUao/X4YU+1B5UpZ3SW0FqRsZUavUclBpU88f1PgmMrNRqPSAwqWc41gmMrNRo6cyplqVUSg0rPnHrpmLLdM/ihyFVUcuX0w61ATmx8pUZGZJMW5FdCeFB4fKWGU7PlvEN4fKWGIzaBSJfzTqAonEGpwbwK/FFqcNly+uEW4CHRDEoN7nb8sCA1uPTMCXC7dAClBvErycFtKGcgHUCpAXQR7XaJsaGcqxE441+pYdyHH26XDCBfTj80CL9DKTUA0U1asKGckeXSAZTqR8tZcjuwWTqEUiWP4IcvSIewo5zRhaw/k46hVMlV0gHAlnJGfiodQCmgDbheOgTYVE4/XAuslI6hat4NpYsyxNlTzsiPpQOomne5dIBetpVzGXpgSMlZiR9as/VmVzn9sAvd91Ryvi8doC+7yhn5MdENdpWqpheBG6VD9GVfOf3weeBK6Riq5vygtGSrNewrZ+R8oF06hKoZLwKXSofoz85y+uFGLPyfpRLLxw87pEP0Z2c5IxcCVnzepBLtCeBq6RADsbecfvgacLF0DJV4SyVXOxiKveWMXEy0jotSlbACP7xFOsRg7C6nH24DstIxVGJ9RTrAUOwuZ+SHwDrpECpxbscP75cOMRT7yxmdNbQEXUJTlU878G/SIYZjfzkB/PBBohlUqXI4Fz98WjrEcOJRzshSYL10CBV7/0dM3ujjU04/bAc+Lh1DxVob8NHSonLWi085AfzwXvS827LatL1ITyEW/1bL4Rz8cIN0iJGKVzkjXyY6FzKx8kXDvt/bxqJr2lh0TRt/3lRgyfIODv9pG+ff3zXo68JOw3HXt3HsdW2ctKyd7lLpNm0vcuTVbTued+lD3Sy4cjtt3Ya7nslTn3Yq/jtZ4LfAT6RDjEb8yhktIXEGYOVZHeXw2KYiH5pbz31nNnPfmc089VqRgoEHlzSzvrXIU68O/Ktf/+cevriwkbtPb2Zqs8OdT+dp7TB85NYO2rrfmB3/tLHAWfMbePilAs31NVHMbcCSuGzO9opfOaF38/Yc6RiVsuKFArc9leewK7ezZHkHv1mf54MH1QFw7Ow6/vDcwOX89IIG/mF29LzN7Ya9mh3SKVh28m5MbHyjhMZATxHufibPcXPqKv8LyTsLP3xWOsRoxbOcAH54MQldTnPBPml+c/puPPTx8fQU4Y6n80yfEP1VTRnnsKlt6AngwefztHYaFs6oY2KjQ0vTzrPjsbPruO3JHmZMTHHiL9r53QarLmMst2/ihzdIhxiL+JYz8glglXSIcnv73immlcr4zn1SbGk3dOSjQm7vNhSH6OZrHYbP3tHJVSeOG/Q5p8ytx1/UyKQmh8ycOm5a11PW/Ba5GThPOsRYxbuc0TV4J5GwRcFOv6WDNRsLFIqGW5/I86Pjm3Zsyq7ZWMCdNPBfW3fB8IH/beeCY5qYNchzej31apHZkx0a65whyx5ja4Az4raf2Ve8ywngh88BHwASs2329aMaOf2WDuZd3sbhM9IsPrCe6x7r4Yt3dXLDX/Jk5tTxl80FvnZv506v++mqHla9XOBbv+9i0TVtLHt84Blxa5dh6vgUb9szzRWPdvP3+yVuv/MV4ET8sG3YZ1rMMSa2byw781s+Q0zO/BiL1g7DPevzvHtWmqnj4/+eWkHdwHvwwwekg+yq5JQTwG+5APCkYyhRH8MPrVzZYLSS9Rbsh+cCP5KOocR8ISnFhKSVM/JZ4FrpEKrqPPzQqkWhd1XyyhkdnVtCQj8DVQM6Dz+8UDpEuSVrn7MvvyVFdJ/Fj0hHURV1Ln6YyKVskltO6C3olcDHpKOosjPA5/HDS6SDVEryNmv78sMicBbwLekoqqwKwCeTXExI+szZl99yGtEdzJqko6hd0gqcih/eLR2k0mqnnAB+y0LgVmBv6ShqTP4CvC8O6/+UQ7I3a/vzwxXAYUTnXap4+TWwsFaKCbVWTug9F/cIYLl0FDVi3wYWlxYZrxm1tVnbl9/iAN8gWtUvLZxGDaydaEGuWF6Puatqt5y9ov3Qa4H9paOonTwKnIkfPi4dRErtbdb2F+2HzgMuIfrsTMnqBr5KtH9Zs8UEnTl35rccTXSvxlnSUWrUw0SbsWulg9hAZ86+/PB3wMFEn4eq6ukiutTvcC3mG3TmHIzf8l7gIuAg6SgJ9yDRNZhPSAexjc6cg/HDu4BDiE7/S/Qi1kKeBE4BjtBiDkxnzpHwW8YBXyC62epE4TRx9yLRR1hX44eJWfepErSco+G37AF8HfgUUC+cJm5eAy4ALsUPO4d7stJyjo3fMpvo5IUPA43CaWy3Hfg+8F38MJQOEydazl3ht+wFfBo4G9hLOI1t1gOXAldpKcdGy1kOfksT8EGizd3DhdNIKgJ3Az8GgtL1tGqMtJzl5rccTFTSU4DdhdNUy7NES8JcjR8+Lx0mKbScleK3pIEjgcXA+wBXNE/5PQHcRnQp1x90liw/LWe1+C2HEBV1MdG5vHHTA9xPVMbb8MNnhPMknpZTgt8yCzgWWAC8k+iUQdtuWNIDrAUeAe4C7i7duFhViZbTBtEBpXlEZe0t7AFU7wyudqLVIVYT3VJxNfA4fthdpfHVALSctvJbmoEZwPTSV9+fe7+mAA0MXeJWYBOwsfTV/+cNwJO6z2gfLWcSRAef6ok2jYulrwJQ0NLFl5ZTKUvpVSlKWUrLqZSltJxKWcq2z9bUEBzHuYzoo5ZWYBrRFTE5YDLwsDHmU3LpVLlpOeOlE/iSMeY+x3HOBGYYY853HGcRcLJoMlV2Ws74uchxnB0zZ6mYk4nW4lEJouWMlzqilQFXARlgT+Aa4FBgrlwsVQn6OWeMOI7zVqIzgwDOISrmK6U/v2iMWSeRS1WGzpwx4ThOC3AF0Zk/EJ1/20x0gjpAB3CcQDRVITpzxojjOHXGmLzjOFOAwBhzeJ//ttYYo2vsJoh+zhkjpWLOBH4OXNz7uOM404GXxIKpitDN2pgolfIXwKvAfxljflt6fBHwA+B7culUJehmbYw4jtNojOnq91gaSBtj9NrLhNFyKmUp3edUylJaTqUspeVUylJaTqUspeVUylJaTqUspeVUylJaTqUspeVUylJaTqUspeVUylJaTqUspeVUylL/DzpcSbOmK6rOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sex =  data.groupby('sex')['user_id'].nunique().reset_index()\n",
    "sex.rename(columns={'user_id':'用户数量'},inplace=True)\n",
    "plt.pie(sex['用户数量'],labels=sex['sex'],autopct='%1.2f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "age = data.groupby('age')['user_id'].nunique().reset_index()\n",
    "age.rename(columns={'user_id':'用户数量'},inplace=True)\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.ylabel('用户数量')\n",
    "plt.title('不同年龄的用户数量')\n",
    "plt.bar(age['age'],age['用户数量'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age_bin</th>\n",
       "      <th>用户数量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(15-20]岁</td>\n",
       "      <td>13726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(20-25]岁</td>\n",
       "      <td>13867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(25-30]岁</td>\n",
       "      <td>13831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(30-35]岁</td>\n",
       "      <td>13802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(35-40]岁</td>\n",
       "      <td>13775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(40-45]岁</td>\n",
       "      <td>13969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(45-50]岁</td>\n",
       "      <td>13535</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age_bin   用户数量\n",
       "0  (15-20]岁  13726\n",
       "1  (20-25]岁  13867\n",
       "2  (25-30]岁  13831\n",
       "3  (30-35]岁  13802\n",
       "4  (35-40]岁  13775\n",
       "5  (40-45]岁  13969\n",
       "6  (45-50]岁  13535"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = [15,20,25,30,35,40,45,50]\n",
    "labels = ['(15-20]岁','(20-25]岁','(25-30]岁','(30-35]岁','(35-40]岁','(40-45]岁','(45-50]岁']\n",
    "data_age = data.copy()\n",
    "data_age['age_bin'] = pd.cut(x=data.age,bins=bins,right=True,labels=labels)\n",
    "age = data_age.groupby('age_bin')['user_id'].nunique().reset_index()\n",
    "age.rename(columns={'user_id':'用户数量'},inplace=True)\n",
    "age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_rfm = data.groupby(['user_id','date'])['amount'].agg('sum').rename(\"消费金额\").reset_index()\n",
    "user_rfm = user_rfm.groupby('user_id',as_index=False).agg({'date':['max',\"count\"] ,'消费金额':'sum'})\n",
    "user_rfm.columns=['user_id',\"最后购买日期\",\"F\",\"M\"]\n",
    "user_rfm['最后购买日期'] = pd.to_datetime(user_rfm['最后购买日期'])\n",
    "user_rfm['R'] = user_rfm['最后购买日期'].apply(lambda x:user_rfm['最后购买日期'].max() - x)\n",
    "user_rfm['R'] = user_rfm['R'].dt.days\n",
    "user_rfm = user_rfm[['user_id','R','F','M']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>1%</th>\n",
       "      <th>10%</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>90%</th>\n",
       "      <th>95%</th>\n",
       "      <th>99%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>R</th>\n",
       "      <td>92755.00</td>\n",
       "      <td>98.59</td>\n",
       "      <td>54.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>61.00</td>\n",
       "      <td>101.00</td>\n",
       "      <td>127.00</td>\n",
       "      <td>180.00</td>\n",
       "      <td>196.00</td>\n",
       "      <td>211.00</td>\n",
       "      <td>321.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>92755.00</td>\n",
       "      <td>2.51</td>\n",
       "      <td>4.56</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>159.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>92755.00</td>\n",
       "      <td>1239.68</td>\n",
       "      <td>4129.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>41.64</td>\n",
       "      <td>148.11</td>\n",
       "      <td>456.89</td>\n",
       "      <td>1141.17</td>\n",
       "      <td>2402.53</td>\n",
       "      <td>3823.67</td>\n",
       "      <td>12744.87</td>\n",
       "      <td>160604.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     count    mean     std  min   1%   10%    25%    50%     75%     90%  \\\n",
       "R 92755.00   98.59   54.53 0.00 2.00 24.00  61.00 101.00  127.00  180.00   \n",
       "F 92755.00    2.51    4.56 1.00 1.00  1.00   1.00   1.00    3.00    4.00   \n",
       "M 92755.00 1239.68 4129.72 0.00 6.00 41.64 148.11 456.89 1141.17 2402.53   \n",
       "\n",
       "      95%      99%       max  \n",
       "R  196.00   211.00    321.00  \n",
       "F    6.00    17.00    159.00  \n",
       "M 3823.67 12744.87 160604.07  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_rfm['user_id'] = user_rfm['user_id'].astype('object')\n",
    "user_rfm=user_rfm.set_index(\"user_id\")\n",
    "user_rfm.describe(percentiles=(0.01,0.1,0.25,0.75,0.9,0.95,0.99)).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1515915625439951872</th>\n",
       "      <td>0.67</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515915625440038400</th>\n",
       "      <td>-1.37</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515915625440051712</th>\n",
       "      <td>-1.72</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>1.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515915625440099840</th>\n",
       "      <td>-1.57</td>\n",
       "      <td>2.08</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515915625440121600</th>\n",
       "      <td>0.58</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        R     F     M\n",
       "user_id                              \n",
       "1515915625439951872  0.67 -0.33 -0.20\n",
       "1515915625440038400 -1.37 -0.11 -0.29\n",
       "1515915625440051712 -1.72 -0.11  1.51\n",
       "1515915625440099840 -1.57  2.08  0.89\n",
       "1515915625440121600  0.58 -0.11 -0.26"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "#采用StandardScaler方法对数据规范化：均值为0，方差为1的正态分布\n",
    "g = StandardScaler()\n",
    "stand_data = g.fit_transform(user_rfm)\n",
    "stand_data = pd.DataFrame(stand_data,columns=user_rfm.columns,index=user_rfm.index)\n",
    "stand_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#根据最小的SSE原则确定簇数量，选择最佳的K值，即客户的类别数量\n",
    "from sklearn.cluster import KMeans\n",
    "#定义SSE列表，用来存放不同K值下的SSE\n",
    "SSE = []\n",
    "#定义候选K值\n",
    "for i in range(1,10):\n",
    "    kmeans = KMeans(n_clusters = i,random_state = 12)\n",
    "    kmeans.fit(stand_data)\n",
    "    SSE.append(kmeans.inertia_)\n",
    "#使用手肘法看K值\n",
    "plt.plot(range(1,10),SSE,marker = 'o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "标签: [0 2 2 ... 2 2 2]\n",
      "SSE: 53980.05809761378\n",
      "迭代次数: 26\n",
      "分值: -53980.058097613786\n"
     ]
    }
   ],
   "source": [
    "#执行聚类\n",
    "labels_num=6\n",
    "kmeans = KMeans(n_clusters=labels_num,random_state=12)\n",
    "result = kmeans.fit(stand_data)\n",
    "#获取每个样本所属的簇，标签的数值对应所属簇的索引\n",
    "print('标签:',kmeans.labels_)\n",
    "#获取SSE（簇惯性）\n",
    "print('SSE:',kmeans.inertia_)\n",
    "#获取迭代次数\n",
    "print('迭代次数:',kmeans.n_iter_)\n",
    "#聚类的分值，分值越大，效果越好,直接取SSE的相反数\n",
    "print('分值:',kmeans.score(stand_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#统计每个类别群体的数量\n",
    "import seaborn as sns\n",
    "qt = pd.Series(kmeans.labels_).value_counts()\n",
    "ax = sns.countplot(x=kmeans.labels_,order=qt.index)\n",
    "#在图像上绘制数值\n",
    "for x,y in enumerate(qt):\n",
    "     text = ax.text(x,y,y)\n",
    "     text.set_ha('center')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1080 with 18 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#客户群体分析\n",
    "fig,ax = plt.subplots(labels_num,3)\n",
    "fig.set_size_inches(18,15)\n",
    "for i in range(labels_num):\n",
    "    #获取第i个客户群的数据\n",
    "    de = stand_data[kmeans.labels_==i]\n",
    "    sns.boxplot(y='R',data=de,ax=ax[i][0])\n",
    "    sns.boxplot(y='F',data=de,ax=ax[i][1])\n",
    "    sns.boxplot(y='M',data=de,ax=ax[i][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "###利用模型预测  先观察数据的正态性和平稳性\n",
    "fig, ax = plt.subplots(figsize=(20,5))\n",
    "data.groupby('date').agg({'amount':'sum'}).plot(ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>T</th>\n",
       "      <th>monetary_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1515915625439951872</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>135.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515915625440038400</th>\n",
       "      <td>1.00</td>\n",
       "      <td>36.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>35.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515915625440051712</th>\n",
       "      <td>1.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>29.00</td>\n",
       "      <td>6517.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515915625440099840</th>\n",
       "      <td>11.00</td>\n",
       "      <td>171.00</td>\n",
       "      <td>184.00</td>\n",
       "      <td>391.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515915625440121600</th>\n",
       "      <td>1.00</td>\n",
       "      <td>59.00</td>\n",
       "      <td>189.00</td>\n",
       "      <td>138.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     frequency  recency      T  monetary_value\n",
       "user_id                                                       \n",
       "1515915625439951872       0.00     0.00 135.00            0.00\n",
       "1515915625440038400       1.00    36.00  60.00           35.39\n",
       "1515915625440051712       1.00    24.00  29.00         6517.37\n",
       "1515915625440099840      11.00   171.00 184.00          391.14\n",
       "1515915625440121600       1.00    59.00 189.00          138.87"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lifetimes.utils import summary_data_from_transaction_data\n",
    "train_df = summary_data_from_transaction_data(\n",
    "    data,\n",
    "    customer_id_col=\"user_id\",\n",
    "    datetime_col=\"date\",\n",
    "    monetary_value_col=\"amount\",\n",
    "    freq=\"D\",\n",
    ")\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>se(coef)</th>\n",
       "      <th>lower 95% bound</th>\n",
       "      <th>upper 95% bound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>r</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>7.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>6.80</td>\n",
       "      <td>7.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.92</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>1.71</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.62</td>\n",
       "      <td>1.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       coef  se(coef)  lower 95% bound  upper 95% bound\n",
       "r      0.24      0.00             0.23             0.24\n",
       "alpha  7.00      0.10             6.80             7.19\n",
       "a      0.92      0.02             0.88             0.95\n",
       "b      1.71      0.04             1.62             1.79"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lifetimes import BetaGeoFitter\n",
    "bgf = BetaGeoFitter(penalizer_coef=0.000001)  \n",
    "bgf.fit(train_df['frequency'], train_df['recency'], train_df['T'])\n",
    "bgf.summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>T</th>\n",
       "      <th>monetary_value</th>\n",
       "      <th>predicted_purchases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1515915625484630528</th>\n",
       "      <td>128.00</td>\n",
       "      <td>298.00</td>\n",
       "      <td>301.00</td>\n",
       "      <td>499.17</td>\n",
       "      <td>58.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515915625484617728</th>\n",
       "      <td>140.00</td>\n",
       "      <td>316.00</td>\n",
       "      <td>318.00</td>\n",
       "      <td>661.45</td>\n",
       "      <td>62.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515915625484649472</th>\n",
       "      <td>129.00</td>\n",
       "      <td>277.00</td>\n",
       "      <td>279.00</td>\n",
       "      <td>439.25</td>\n",
       "      <td>63.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515915625484627712</th>\n",
       "      <td>148.00</td>\n",
       "      <td>305.00</td>\n",
       "      <td>307.00</td>\n",
       "      <td>778.31</td>\n",
       "      <td>67.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515915625484582912</th>\n",
       "      <td>158.00</td>\n",
       "      <td>317.00</td>\n",
       "      <td>319.00</td>\n",
       "      <td>417.16</td>\n",
       "      <td>69.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     frequency  recency      T  monetary_value  \\\n",
       "user_id                                                          \n",
       "1515915625484630528     128.00   298.00 301.00          499.17   \n",
       "1515915625484617728     140.00   316.00 318.00          661.45   \n",
       "1515915625484649472     129.00   277.00 279.00          439.25   \n",
       "1515915625484627712     148.00   305.00 307.00          778.31   \n",
       "1515915625484582912     158.00   317.00 319.00          417.16   \n",
       "\n",
       "                     predicted_purchases  \n",
       "user_id                                   \n",
       "1515915625484630528                58.80  \n",
       "1515915625484617728                62.09  \n",
       "1515915625484649472                63.40  \n",
       "1515915625484627712                67.48  \n",
       "1515915625484582912                69.88  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = 180 \n",
    "train_df['predicted_purchases'] = bgf.conditional_expected_number_of_purchases_up_to_time(t, train_df['frequency'], train_df['recency'], train_df['T'])\n",
    "train_df.sort_values(by='predicted_purchases').tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lifetimes.plotting import plot_period_transactions\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "plot_period_transactions(bgf, max_frequency=60, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from dateutil import relativedelta\n",
    "period_months = 6\n",
    "date1 = data.date.max() - relativedelta.relativedelta(months=period_months)\n",
    "train = data[data.date <= date1]\n",
    "test = data[data.date > date1]\n",
    "\n",
    "from lifetimes.utils import calibration_and_holdout_data\n",
    "from lifetimes.plotting import plot_calibration_purchases_vs_holdout_purchases\n",
    "\n",
    "summary_cal_holdout = calibration_and_holdout_data(data, 'user_id', 'date',\n",
    "                                        calibration_period_end=train.date.max(),\n",
    "                                        observation_period_end=test.date.max() )\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "bgf.fit(summary_cal_holdout['frequency_cal'], \n",
    "        summary_cal_holdout['recency_cal'], \n",
    "        summary_cal_holdout['T_cal'])\n",
    "plot_calibration_purchases_vs_holdout_purchases(bgf, summary_cal_holdout, n = 30,  ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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